Systems and Related Apparatus for Improving Process Data Integrity and Timeliness

ABSTRACT

Systems, apparatus, and methods for improving the monitoring of processes. These systems comprise processors co-located with the (physical) processes and which accept certain metrics regarding the processes. These systems also include processors which are located remotely from the processes, but, which are in communication with the co-located processors. The remote processors receive the metrics from the co-located processors and transform these metrics to corresponding probability metrics. Based on the probability metrics, the remote processors determine whether the processes are predictable. They also output the predictability determinations regarding the processes. The processes can be associated with petrochemical wells, food/beverage processes, etc. Additionally, or in the alternative, some systems further comprise memories which store flowchart representations of the processes. In various embodiments, the remote processors associate the metrics with the operations represented in the flowcharts.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application No.62/196,040, filed on Jul. 23, 2015, entitled Enterprise PerformanceReporting Systems, by Forrest Breyfogle, the entirety of which isincorporated herein as if set forth in full.

BACKGROUND

In large industrial processes, things go wrong. And sometimes they gowrong catastrophically. For instance, the Apr. 20, 2010, DeepwaterHorizon Explosion (and subsequent fire and oil spill) killed 11 people.It also spewed approximately 5 million barrels of oil into the Gulf ofMexico. To make matters worse, British Petroleum (the owner of thenDeepwater Horizon rig) had a history of major accidents and lagged otheroil companies when it came to safety, according to federal officials andindustry analysts. See Jad Mouawadmay, the New York Times, May 8, 2010.Perhaps not as dramatic, but also serious, the 2015 Blue Bell listeriaincident was both complex and unusual in that the ten illnesses overfour states spanned the period from 2010 to 2015. All involvedhospitalizations and three died. See Dan Flynn, Food Safety News, Mar.27, 2016.

And of course, almost every American old enough to remember the SpaceShuttle Challenger disaster will remember Jan. 28, 1986 as one of themost tragic days in recent American history. For, in addition to the 6Astronauts on board, the accident killed Christa McAuliffe, who was tohave been America's first “Teacher in Space.” This catastrophe shut theSpace Shuttle program down for 3 years, put America's manned spaceprogram on hold, and resulted in $350 million of re-design work. Se theWashington Post, Mar. 12, 1986.

Yet all of these incidents could have been prevented had managementand/or oversight personnel had timely, up-to-date information from the“field” that had not been manipulated and/or obscured by thoseresponsible for allowing the incidents to occur. Unfortunately, for allinvolved, these organizations lacked a physical system to ensure theintegrity and timeliness of safety-related data flowing to management.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosed subject matter. Thissummary is not an extensive overview of the disclosed subject matter,and is not intended to identify key/critical elements or to delineatethe scope of such subject matter. A purpose of the summary is to presentsome concepts in a simplified form as a prelude to the more detaileddisclosure that is presented herein. The current disclosure providessystems, apparatus, methods, etc. for monitoring processes and, moreparticularly, for improving the integrity and timeliness of informationrelated to those processes.

Some embodiments provide systems for monitoring physical processes.These systems comprise processors co-located with the physical processesand which accept safety-related metrics regarding the physicalprocesses. These systems also include processors which are locatedremotely from the physical processes, but, which are in communicationwith the co-located processors.

The remote processors receive the safety-related metrics regard from theco-located processors and transform the safety-related metrics tocorresponding probability metrics. Based on the probability metrics, theremote processors determine whether the physical processes arepredictable. They also output the predictability determinationsregarding the physical processes.

In some embodiments, the physical processes are associated withpetrochemical wells, food/beverage processes, etc. Additionally, or inthe alternative, some systems further comprise memories which storeflowcharts representations of the physical processes and the remoteprocessors associate the probability metrics with operations of thephysical processes. In various embodiments, the remote processorsassociate the safety-related metrics with the representation of thephysical processes.

Systems of embodiments comprise processors co-located with certainprocesses and which accept metrics regarding the processes. Thesesystems also include processors which are located remotely from theprocesses, but, which are in communication with the co-locatedprocessors. The remote processors of the current embodiment receive themetrics regard from the co-located processors and transform the metricsto corresponding probability metrics. Based on the probability metrics,the remote processors determine whether the processes are predictable.They also output the predictability determinations regarding theprocesses.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with the annexedfigures. These aspects are indicative of various non-limiting ways inwhich the disclosed subject matter may be practiced, all of which areintended to be within the scope of the disclosed subject matter. Otheradvantages and novel features will become apparent from the followingdetailed disclosure when considered in conjunction with the figures andare also within the scope of the disclosure.

BRIEF DESCRIPTION OF THE FIGURES

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberusually identifies the figure in which the reference number firstappears. The use of the same reference numbers in different figuresindicates similar or identical items.

FIG. 1 illustrates a system for monitoring a process.

FIG. 2 illustrates a drilling rig.

FIG. 3 illustrates a value chain supporting a physical process.

FIG. 4 illustrates a control chart related to the physical process.

FIG. 5 illustrates an incident chart regarding the operations of aphysical system.

FIG. 6 illustrates a probability chart regarding the operations of thatphysical system.

FIG. 7 illustrates another incident chart regarding the operations of aphysical system.

FIG. 8 illustrates another probability chart regarding the operations ofthat physical system

FIG. 9 illustrates a flowchart of a method for improving, inter alia,the integrity and/or timeliness of data regarding a physical processes.

FIG. 10 illustrates a graphical user interface for monitoring a process.

FIG. 11 illustrates a component of a graphical user interface formonitoring a process.

FIG. 12 illustrates another component of a graphical user interface formonitoring a process.

FIG. 13 illustrates yet another component of a graphical user interfacefor monitoring a process.

FIG. 14 illustrates another component of a graphical user interface formonitoring a process.

FIG. 15 illustrates a taxonomy of a system for monitoring a process.

FIG. 16 illustrates a process goal setting worksheet for severalscenarios.

FIG. 17 illustrates a stoplight scorecard for one scenario in FIG. 16.

FIG. 18 illustrates an “individuals” I-chart and a probability chart fora scenario.

FIG. 19 illustrates another individuals I-chart and another probabilitychart for a scenario.

FIG. 20 illustrates yet another stoplight scorecard for a scenario.

FIG. 21 illustrates an individuals I-chart and a probability chart for ascenario.

FIG. 22 illustrates another I-chart and another probability chart for ascenario.

FIG. 23 illustrates yet another I-chart and yet another probabilitychart for a scenario.

FIG. 24 illustrates another stoplight scorecard for a scenario.

FIG. 25 illustrates yet another I-chart for a scenario.

FIG. 26 illustrates still another I-chart for a scenario.

FIG. 27 illustrates a stoplight scorecard for a scenario.

FIG. 28 illustrates an I-chart for a scenario.

FIG. 29 illustrates a stoplight scorecard.

FIG. 30 illustrates an I-chart.

FIG. 31 illustrates a stoplight scorecard.

FIG. 32 illustrates an I-chart.

FIG. 33 illustrates data regarding a process.

FIG. 34 illustrates more data regarding a process.

DETAILED DESCRIPTION

This document discloses systems, apparatus, methods, etc. for monitoringprocesses and, more particularly, for improving the integrity andtimeliness of information related to those processes inter alia.Embodiments provide centralized tools for monitoring, reporting on,controlling, and improving processes.

Take, for instance, the many processes that occur in a company producingpetrochemicals. These processes occur within a context such as thatshown by FIGS. 1 and 2. And at this juncture, it might be helpful tobriefly discuss FIGS. 1 and 2 to facilitate the current disclosure. FIG.1 illustrates a system for monitoring a process. More particularly, FIG.1 illustrates system 100, companies 102, affiliates 104, fields 106,drilling rigs 108, wells 110, wellbores 112, numeric drilling data 114,descriptive drilling data 116, reams of data 118, event 120, user 122,data repository 126140, and a “remote” computer 140 among other aspectsof system 100. As is disclosed herein, system 100 generates andprocesses drilling data (for instance numeric drilling data 114 and/ordescriptive drilling data 116 as well as other types of data) to provideinformation (perhaps including raw or processed drilling data 114 and116) to user 122. Meanwhile, FIG. 2 illustrates a drilling rig. Asshown, the drilling rig 108 includes mast or derrick 200, drill string202, hook 204, rotary table 206, connection 208, drill bit 212, and mudmotor 214 among many other potential components. In addition, FIG. 2illustrates that the drilling rig 108 can include a computer or datasystem 216 (co-located with the processes on the rig 108 and) networkedwith a web server 218 which further comprises network interface 220,processor 222, memory 224, and user interface 226. FIG. 2 illustratesthat either, or both of, the data system 216 of the drilling rig 108 andthe web server 218 can be in communication with a data repository 227.Note that these computer related pieces of equipment and/or data can allbe networked with remote computers 140 located elsewhere in the system100. This particular drilling rig 108 also includes (or uses) aninstrument package 228, a log 230, and a pseudolog 232.

Turning to the rig first, some of the processes on the rig include:

-   -   Drilling wells and or well bores    -   Finishing the wells    -   Producing oil, gas, water, etc.    -   Maintaining/repairing the rig etc.

Of course, other processes occur on the rig. For instance, some of theseother processes include:

-   -   Treating sick/injured crew members    -   Creating/maintaining production records    -   Auditing safety related conditions    -   Investigating safety-related issues    -   Reviewing crew member performance and rewarding/punishing the        same    -   Tracking crew members' working hours.

And, of course, data and/or information is sent to the company 102headquarters, affiliates 104, vendors, regulators, other governmentalorganizations, etc. via the network 233. Elsewhere in the company 102illustrated by FIG. 1, many other processes occur.

-   -   For instance, customer inputs are sought on the products/service        of the company.    -   Developing products    -   Marketing those products    -   Selling those products    -   Producing/manufacturing those products    -   Delivering those products    -   Invoicing/collecting payments    -   Reporting financials and other information

But many of these processes depend on the data, information, etc.flowing from the rig 108 or other locations. As a result, users 122co-located with the data sources have the opportunity to manipulate thatdata. Many motivations might exist for them to do so: some benign, somenot. For instance, some users 122 might be unaware of problems with aprocess and therefore might not be disciplined in collecting dataregarding that issue. Other users 122 might be aware of the problem, butmight be unable/unwilling to acknowledge it. Still other users 122 mightthink that hiding or obscuring the problem might be desirable bythemselves or others. Other users 122 might even be willing todeliberately hide data that might reveal a problem to management (atheadquarters or elsewhere) and/or others. Of course, such behaviors makeit more difficult for these remote (for instance at the company 102headquarters, a governmental office, etc.) users 122 to find and correctproblems.

Now with reference to FIGS. 3-32, systems for improving the integrityand timeliness of data and/or information reaching remote users 122 suchas management are disclosed herein. More specifically, FIG. 3illustrates a value chain 300 supporting a physical process 301 andrelated metrics 304, support processes 306, and other processes such as:obtaining customer input 310, developing a product 312, marketing theproduct 314, selling the product 316, invoicing 320, and reporting 322.Often, the value chain 300 describes everything that a particularcompany 102 does although they can be centered about a particularphysical process 301. For instance, the physical process 301 could beoperating a drilling rig 108 or other industrial activity, operating anaerospace system such as a launch vehicle, producing a food/beveragesuch as ice cream, etc. Note that in this instance of a value chain 300,the processes represented by rectangles and interconnected by lineswithout arrowheads tend to be support functions such as safety,information technology, enterprise process management, etc. The metrics304 represented by oblong or rounded rectangles are top-level metricsfor the company 102/value chain 300 and can correspond to the variousprocesses 302 whether they be physical, support, or otherwise.

Indeed, a remote processor 140 can associate various metrics 304 withtheir corresponding processes 302. Moreover, graphic user interfaces(GUIs) running on the remote computer 140 or processor can display thevalue chain 300 and/or select portions thereof and allow users to selectprocesses 301, 310, 312, 314, 316, 318, and/or 320. Responsive thereto,the remote computer 140 can display the metric(s) 304 associated withthe selected process 302. A user could, for instance, select a GUIrepresentation of physical process 301 and view one or more metrics 304such as lead time-related, defect rate, work in progress (WIP), deliverytime performance, throughput, etc. metrics 304. Moreover, as isdisclosed elsewhere herein, various other metrics 304 such assafety-related metrics 304 can also be available for viewing via theremote computer 140.

FIG. 4 illustrates a control chart related to the physical process 301.The control chart 400 illustrates data gathered regarding a metric 304related to the particular process 301 (or perhaps group of processes).In this instance, the control chart plots the number of (safety-related)incidents occurring in a time interval (in this case the number ofincidents in a month). The raw data underlying the control chart 400 isfound in Table 1 (FIG. 33). While the control chart 400 provides aconvenient, visual method to portray the underlying data, it provideslittle (if any) stable/predictable statement, etc. Typical usage of acontrol chart is to determine if a process is ln control and react topoints that indicate out of control conditions. With 30000 foot levelthe system of embodiments also displays a probability plot forcontinuous data so that it can make a prediction statement. Forattribute data we do not necessarily need a probability plot. But, thesystem still makes a prediction assessment, which again is not done withheretofore available systems/

Table 2 (FIG. 34) and FIG. 6, on the other hand do provide informationinsight to the user as to how the physical process 301 is behaving,since FIG. 5 indicates predictability of the physical process 301 asnoted below. In addition to the number of safety-related incidentsoccurring in any given month, Table 2 shows the calculateddays-since-last incident associated with the data in Table 1. And FIG. 5illustrates an incident chart regarding the operations of a physicalsystem and reflecting the days-since-last-incident information fromTable 2. More specifically, FIG. 5 illustrates an incident chartregarding the operations of a physical system, which assesses processstability. But, while FIG. 5 provides a convenient way to view the dataof Table 2 and provides insight into whether the underlying physicalsystem is stable/predictable or not. In this scenario, the process isstable since there are no trends or excursions relative to thestatistically calculated upper control limit (UCL) or lower controllimit (LCL).

FIG. 6 illustrates a probability chart regarding the operations of thatphysical system, which provides information for a process-predictionstatement at the bottom of the FIGS. 5 and 6 chart pair, since FIG. 5indicates that the process is stable. More specifically, FIG. 6 replotsthe days-since-last-incident data in a probability chart. From FIG. 6therefore, it is seen that for the 20 time-between-failures data pointspresented, the estimated median days-between incidents is 84 with 80% ofincidents occurring between 50 and 117 days. If this frequency ofincident-occurrences is unsatisfactory, it might be desirable to dosomething to improve the process. Thus, taken together, the I-chart(FIG. 5) and probability chart (FIG. 6) allows the system tomathematically determine whether the process is under control and tooutput a prediction statement to that effect. More particularly, thesystem can do so by calculating the standard deviation, AD value,p-value, and/or other paramaters associated with the process andcomparing them to user input limits. These capabilities of the system ofthe current embodiment are particularly useful when large numbers ofdata points and/or when the data points are generated rapidly. Anindication that an improvement was made to the frequency between eventsis that the individuals chart (I-chart) transitions to an enhanced levelof performance.

As a result, users 122 associated with the physical process 301 mightdecide to improve the performance of the physical process 301. Forinstance, they could add safety features to the equipment associatedwith the physical process 301; they could institute better trainingbased on simulations or otherwise; etc. But, in this scenario, they makechanges associated with the physical process 301 and collect more datarelated to the physical process 301 and its associated metric 304. Thedata collected following improvements to the physical process 301 arepresented in Table 2 as the last 6 data points. FIGS. 7 and 8 illustratethe results in another incident chart and a probability chartrespectively. In FIG. 7 (the incident chart), the 6 new data points areadded in a separate section of the chart (and are re-scaled) while inFIG. 8, the post-improvement data points are presented standing alone.

FIG. 7 illustrates another incident chart regarding the operations of aphysical system, which assesses process stability, noting that a changeoccurred in the system. And FIG. 8 illustrates another probability chartregarding the operations of that physical system, which providesinformation for a process-prediction statement at the bottom of theFIGS. 7 and 8 chart pair, since process indicates stability sinceprocess shift noted in FIG. 7.

The incident chart (FIG. 7) provides insight as to whether theunderlying physical process is stable and/or changed over time. If anindividuals chart has a recent region of stability, one can say that theprocess is predictable. The probability chart (FIG. 8) provide insightinto what one might predict, if the individuals chart indicates currentprocess stability. More particularly, FIG. 8 shows that the median forthe days-since-last-incident has risen to 113.3 with now an 80%frequency of occurrence between 106 and 121 days; i.e., the time betweenfailures for 4 out of 5 incidents is between 106 and 121 days. Thus, thepost-improvement probability chart (FIG. 8) when compared to thepre-improvement probability chart (FIG. 6) provides an estimate of thebenefits of the change.

Thus, embodiments provide improvements in creating honest, transparent,performance metrics (from a process point of view). And indeed, systemswhich do so can be obtained from Smarter Solutions, Inc. of Austin, Tex.under their Integrated Enterprise Excellence® (IEE) and/or EnterprisePerformance Reporting Systems (EPRSs) lines of products/services. And,it is noted that, by having such honest, transparency in the reportingof performance measurements with various physical process, companiesand/or other organizations can avoid potentially disastrous results. Oneneed look no further than the Blue Bell listeria, Deepwater Horizon, andSpace Shuttle Challenger incidents to appreciate thesavings/cost-avoidance in terms of capital as well as the safeguardingof physical structures and life and limb provided by embodiments. Incontrast, with systems heretofore available typical organizationalreporting can lead to ineffective if not destructive behaviors such asthat which occurred during these incidents. Leading up to and duringthese incidents it was as if, colloquially, there was an “elephant inthe room” that few involved were willing to acknowledge and/or address.

Embodiments automatically update predictive, top-level performancemetrics and therefore provide transparency in the reporting of the same.Some embodiments also (or in the alternative) integrate the predictivemetrics with the processes via GUI links for convenient selection andviewing. Moreover, leaders in the organization (using remote processorsand/or systems disclosed herein) decide which top-level metrics 304 areto be automatically updated thereby providing those with accessprivileges to these metrics 304 information in a timely manner (from aprocess point of view). Moreover, systems of embodiments allow“real-time clickable” access to such process-reported metrics duringperiodic (for instance, monthly) meetings and/or at times selected bysuch users.

Thus, embodiments help prevent field and/or co-located users 122 fromcreating “filtered” reports regarding unfavorable situations pertainingto physical processes 302, other processes 302, and/or other situationsin organizations which have implemented such systems. Systems ofembodiments therefore help prevent co-located users 122 from shieldingremote users 122 (for instance, management) from learning of true fieldconditions which might/might not be adverse to the correspondingorganizations (and/or the reputations of the co-located users 122).

In systems of various embodiments, the corresponding value chains 300can include metrics 304 for the safety function. These metrics caninclude those related to safety incidents and/or frequency of safetyaudits and other corrective and/or prophylactic activities.Organizations that are not trying to hide “bad news” should be receptiveto such systems. And sub-groups attempting to hide “bad news” willlikely find it much harder to do so if the over-arching organization hasimplemented a system in accordance with embodiments. Moreover,embodiments provide improvements over heretofore available systemswhich, at best, provide rough performance measures such asred-yellow-green scorecards. Even well-intentioned organizations thatrely upon these rough measurements often do so to their detriment.Instead, systems of embodiments provide improved metrics 304 consistentwith process thinking/reporting, structured process improvements, andbetter common-cause variability. And, when the sought after, processimprovements are not satisfactory, embodiments provide metrics 304 whichindicate the same and allow users 122 to track progress as theyimplement further process improvements.

Now with reference to FIG. 9, various embodiments provide reportingsystems as disclosed herein. More specifically, FIG. 9 illustrates aflowchart of a method for improving, inter alia, the integrity and/ortimeliness of data regarding physical processes. Embodiments provideIntegrated Enterprise Excellence® (IEE) and/or EPRS Lean Six Sigma®(registered by Motorola) systems which can be obtained from SmarterSolutions, Inc. of Austin, Tex. Such systems can be created byprogramming computers, web servers, web browsers, etc. to implement thesame. Such specially programmed systems (as opposed to generic computersand the like) can provide comprehensive enterprise monitoring which isreported to organization managers (and/or other authorized users) viainteractive enterprise performance dashboards and/or other GUIs.

Some systems provide a number of features including, but not limited to:

-   -   Automated data acquisition from enterprise resource planning        (ERP) systems and other data resources.    -   Manual data acquisition for other data resources if desired.    -   Creation of organization performance metrics 304 using available        data.    -   Automated updates of enterprise performance metrics 304.    -   Online tools for development of flowcharts and value chains 300        that illustrate enterprise processes.    -   Linking of flowcharts and value chains 300 to related metrics        304, files and other resources.    -   Providing high-level project status tools.    -   Administration tools for software configuration and user access        control.    -   Provision for organizations to load their logo or other branding        images.    -   Linkage to websites, articles, standard operating procedures and        other information that may be relevant to an organization's        value chain

Users who might benefit from such systems include, but are not limitedto:

-   -   Medium and large organizations with a culture or strong desire        for excellence (and even other organizations can benefit).    -   Organizations of any size applying the principles of IEE® or        Lean Six Sigma®.    -   Leaders of these organization who seek tools for themselves and        their staff to easily assess process performance.    -   Leaders of these organization who seek “enterprise performance        dashboards” and similar tools for themselves and their staff.    -   Lean Six Sigma® Practitioners who seek a centralized tool to        control, report and monitor their projects.    -   IEE® Lean Six Sigma® Practitioners who seek a centralized tool        to control, report and monitor their projects    -   Organizations that desire to implement and an operational        excellence system

Organizations typically strive to achieve the “3 Rs” of business; i.e.,everyone doing the right things, doing them right, and doing them at theright time. To move toward achievement of these objectives in an everchanging complex, competitive climate, businesses could use a frameworkfor orchestration of activities and business improvement efforts. IEEsystems of the current embodiment provide such frameworks as disclosedherein.

Governance methods such as those illustrated by FIG. 9 provide roadmapsfor systematically addressing management challenges head on if desired.Methods are provided that can facilitate process improvement efforts andthat can positively impact the enterprise as a whole. Method 900 (seeFIG. 9) includes various activities such as describing a vision and/ormission for the organization, a physical process 301, a physical system,and/or some other process 302. See reference 902. Method 900 alsoincludes, as shown at reference 904, describing a value chain 300including top level metrics 304 (, “satellite level” Metrics® and/or“30,000 foot level Metrics® as registered by Smarter Solutions, Inc. ofAustin, Tex.). Moreover, reference 906 illustrates that (in accordancewith the current embodiment) method 900 also comprises analyzing theprocess 302 or other object of interest (hereinafter “process”).

FIG. 9 also illustrates that method 900 comprises establishing “SMART”(specific, measurable, action oriented, reasonable, and timely)top-level, metric-related goals for the process. See reference 908. Asreference 910 illustrates, method 900 further comprises createstrategies for achieving the vision and/or mission chosen for theprocess 302. Moreover, reference 912 indicates that areas of highpotential for improvement can be identified and that SMART top levelmetrics for those areas can be established. Reference 914, meanwhile,shows that method 900 comprises identifying and executing processimprovement projects for those areas of potential. In addition, FIG. 9illustrates that method 900 comprises assessing the impact of thoseprojects (using the metrics established during method 900 or otherwise)during their implementation and/or after their completion. See reference916. Method 900, moreover, illustrates that those improvements can bemaintained (again with reference to pertinent metrics 304,) Seereference 918. Method 900 also includes a feedback loop by which method900 can be repeated/continued in whole or in part as might be desired.See reference 920.

Indeed, in accordance with embodiments, that feedback loop can return toreference 906 (analyzing the process) rather than returning to reference902 (although that too is in accordance with embodiments). Oneimplication of this type of feedback is that a long-lasting front-endmanagement system is provided, which can remain structurally constantover time even through leadership, organizational, strategy, and/orother process-related changes occur.

EPRS systems provided herein can transform heretofore available processdashboards/scorecards to predictive performance metrics 304 that can beautomatically updated. Such systems can also allow users to tailorreports so that everyone in the pertinent organization(s) can viewup-to-date information on how processes of interest are performing andcan view graphic representation of the processes that are leading tothese results shown by those metrics 304. Furthermore, EPRS systems inaccordance with the current embodiment can links those processrepresentations (and documentation related thereto) with the pertinentmetrics 304 from these efforts in a timely manner. Such features allowusers 122 working to develop the metrics 304 while other users 122(working remotely from the other users) develop the processdocumentation and/or process improvements. EPRS systems can bring theseefforts together so that there is a timely flow of information regardingthe processes and the related metrics.

EPRS systems of embodiments allow organizations associated with theprocess(es) to transition from “firefighting” (in which common-cause(normal) variability of the process 302 is reacted to as though it werea special cause) to “fire-prevention” (in which long term processimprovements cause benefits at the top-levels as well as perhaps otherlevels associated with the process 302). Such EPRS systems alsofacilitate implementing Demings' philosophy, achieving Malcolm BaldrigeAwards, and/or certifying the processes as ISO-9000 compliant.

Moreover, organizations can use the systems and methods described hereinto move toward achieving the 3 Rs of business (described elsewhereherein). They can also become more efficient and/or effective as thesesystems make it more difficult for users 122 to “play games” with themetrics in attempts to hide, obscure, or otherwise avoid reporting badnews regarding various processes. As a result, systems and/or methods inaccordance with embodiments help prevent conditions which lead toresults like the Deepwater Horizon, Blue Bell, and/or Space ShuttleChallenger incidents. In all of these cases, issues had been known by atleast some users. But the organizations avoided dealing with the issuesbecause of a lack of honest, timely, pertinent, and/or predictiveprocess metrics 304. Thus, risks were run—and ultimately materialized.Systems and methods provided herein help organizations avoid those risksand their results. EPRS systems and methods can also allow organizationsto evaluate performance metrics 304 from a process point of view (in atimely and systematic fashion) so that enhancement efforts areundertaken that will likely benefit the big picture.

With reference now to FIGS. 10-15, it might now be useful to discussGUIs programmed in accordance with embodiments. For instance, FIG. 10illustrates an EPRS dashboard. The dashboard 1000 is an entry point forthe various users of the EPRS system of the current embodiment. Generalaccess users 122 may use this screen to review the current status oftheir process(es). The screen includes options to easily search forinformation via a search field in the header or using various selectionfeatures. Additionally, this screen is a portal to other features toaccess information and/or lists of available metrics 304 and valuechains 300. Furthermore, editors and administrators can use this screento enter the backend of the software that allows their users tomanagement the information available on the EPRS dashboard and/or toadminister the EPRS system and/or underlying software. FIG. 10 alsoillustrates the IEE scorecard 1002 which is a section of the EPRSdashboard 1000 and that can provide a quick summary of metrics 1004identified to be of some user selected level of importance to theorganization (for instance, top-level). Selecting one or more of thesemetrics 304 prompts the system to provide a popup with more detailedinformation about the selected metric.

Meanwhile the value chain section 1008 of the EPRS dashboard allowsusers 122 to select value chains 300 using the value chain informationfeature 1010. They can also (or in the alternative) view processes 1030and/or sub-processes 1032 (or rather graphical representations of thesame such as flowcharts), related metrics 304 and other related links toprocess-related documentation, websites, files and/or other resources.Indeed, if a user selects one or more of the processes 1030 orsub-processes 1032, the dashboard 1000 (or GUI) can be configured suchthat the metric 304 (and/or other information pertinent to the selecteditem) pops up or is otherwise displayed.

The related metrics section 1012 of the EPRS dashboard provides that,when a particular value chain(s) is selected by the user 122, themetrics 304 associated with (i.e., linked to) the pertinent flowchart(for the selected value chain) are displayed in this section of the EPRSdashboard 1000. Moreover, the dashboard 1000 also includes an IEEprojects section 1014. This section of the dashboard 1000 provides users122 the ability to profile high level project status to users 122. Inthis section, moreover, project owners may edit the information andstatus of the projects they are responsible for and in a timely fashion.

With reference to FIG. 11, the customer logo setup option 1100 of thedashboard 1000 allows users 122 to upload a logo or other graphic foridentifying the organization associated with the process(es) associatedwith the dashboard 1000 and/or for customizing the dashboard 1000. Inaccordance with the current embodiment, the dashboard 1000 also provideslinks/controls to list all metrics 304 published in the system, to listall flowcharts published in the system, and/or to publish the flowchartsof various value chains 300 (and/or related metadata).

In the meantime, FIG. 12 illustrates a metric editor 1200 of an EPRSsystem of embodiments. Using it, users 122 with metric-editingpermission can manage and publish metrics. These metrics 304 aretypically created using commercially available statistical products suchas Minitab. Although, in accordance with embodiments, the metrics 304can be created by other means.

FIG. 13 illustrates a flowchart designer feature 1300. Using it, userswith process editing permission can create flowcharts using variousflowcharting tools. Once published, updates to the flowchart(representing various processes) are automatically viewable viaassociated value chains 300 which are selectable via the EPRS dashboard1000 and/or its various features.

FIG. 14 illustrates a project editor. Using the project editor 1400,users with project editing permissions can manage project summaries andstatus information displayed on the EPRS dashboard 1000. FIG. 15illustrates a taxonomy of an EPRS system. The taxonomy 1500 shows thetypes of information, GUIs, etc. present in EPRS systems of embodiments.

Non-Limiting Illustrative Scenarios

At this juncture it might be useful to disclose aspects of someembodiment and how they can relate to Lean Six Sigma process improvementefforts. more specifically, an “individuals” control chart (XmR chart,I-chart) can be used for time-series tracking of a process to determineif the process is in statistical control and whether it can beconsidered stable. When a process is considered stable, in accordancewith embodiments, it experiences only common-cause variability. When aprocess is not in control, special-cause conditions can be causingnon-stability.

In considering a process, it might be desirable to understand andresolve, when appropriate, special-cause conditions. A process that onlyexperiences common-cause conditions does not imply that the process doesnot have any issues. A process can be stable but be unable to provide aconsistent level of quality or performance. Assessments for processstability and capability can be provided through 30,000-foot-levelreports with predictive measurements as further disclosed herein.

A moving range chart can be included with an individuals control chartreport-out, producing a pair of charts (i.e., XmR control chart or ImRcontrol chart). However, since the primary purpose of the MR chart isonly to identify larger than normal short-term swings in the data, thischart will not be included in the report-outs for the scenariosdisclosed below so that the overall reporting and evaluation process canbe simplified.

With reference now to FIGS. 16-32, several non-limiting scenarios areprovided for illustrative purposes. More specifically, thesenon-limiting scenarios illustrate some of the benefits of a30,000-foot-level predictive performance reporting systems ofembodiments.

On that note, executives are often presented a monthly summary of thecurrent level of key performance indicators (KPIs) or metrics 304 intheir organizations. Often information from this goal setting theoryapproach is presented as a PowerPoint presentation where the lastmonth's data is reported with possibly some previous months.Red-yellow-green scorecards may be used to track against goal settingobjectives; however, stoplight scorecarding has issues which will laterbe illustrated through the goal setting worksheet scenarios. Traditionalgoal setting and track-reporting against these goals can:

-   -   Divert much resource from other tasks that are important to the        business.    -   Provide only historical observations (i.e., no prediction        statement).    -   Variance to a goal that may have been arbitrarily set and does        not have direct aligned to overall business needs.    -   Are dated relative to the timeliness of the information        presented.

Instead of using a stoplight goal setting theory approach to scorecards,organizations gain much benefit when they use a 30,000-foot-levelreporting approach that provides, when appropriate, a predictionstatement of what could be expected in the future if nothing changes. Ifthere is process stability with 30,000-foot-level reporting and theresponse is undesirable, some form of structured process improvementefforts are needed.

The following six goal setting worksheet scenarios illustrate thebenefits of 30,000-foot-Level® predictive performance metric reportingover a traditional format, where these metrics can be automaticallyupdated so that anyone authorized can get ready access to the metricsthrough a click of the mouse using Enterprise Performance ReportingSystem (EPRS) software.

In the following real goal setting scenarios, red-yellow-greenscorecards are shown to have issues that can be resolved through30,000-foot-level reporting. Using a goal setting worksheets like theone shown in FIG. 16 is common. However, this form of reporting can leadto much firefighting where the process is not really improved. Thequestion is how would one undertake the objective to have the businessgoal setting worksheet scenarios improved? One could respond to thisquestion as:

-   -   Examine the data from a process point of view.    -   Create a predictive performance metric 304 statement for the        process 302 whenever possible.

The need for predictive metrics 304 is supported by the article GartnerSays Organizations Using Predictive Business Performance Metrics WillIncrease Their Profitability 20 Percent by 2017. See the Gartner Inc.website “Newsroom” article dated Jan. 16, 2014.

What is described below is how six of these metrics 304 could bereported in a predictive performance metric 304 format. The approachthat will be used for this reporting is 30,000-foot-level for the datashown in FIGS. 17, 20, 24, 27, 29, and 31.

Scrap Costs of Production: Goal Setting Scenario 1

FIG. 17 illustrates a traditional stoplight scorecard for one scenarioin FIG. 16. A highlight from the table above scrap costs of productionreporting one observes the data seen in FIG. 17. With this report-outformation, one is to take action to determine what specifically occurredwhen the goal was not met for a month; i.e., the color was red. Onedoesn't know what action, if any, occurred by simply looking at thisdata. However, from this form of reporting one does statistically know:

-   -   If the process was really improved with a transition from red to        green.    -   Know what might be expected in the future.

Let's now examine this data using a 30,000-foot-level reporting forscrap costs as presented in FIG. 18. FIG. 18 illustrates an individualschart with a probability chart for a scenario, along with anon-conformance prediction rate relative to a specification for theassociated process. FIG. 18 therefore illustrates an alternative orsupplement to the reporting illustrated by FIG. 17. A predictionstatement is included at the bottom of the chart shown in FIG. 18. With30,000-foot level reporting, one first notes that scrap costs is acontinuous variable. The first assess is for process stability, whilethe next effort is provide a prediction statement when the process 301is stable. For this continuous-data situation, the 30,000-foot-levelindividuals control chart assesses stability, while a probability plotprovides a prediction statement. With 30,000-foot-level reporting thereis also a statement at the bottom of the report-out which provides aprediction statement, when appropriate.

From this 30,000-foot-level report-out, one has no reason to state thatthe process 301 is not stable. Data from the recent region of stabilitycan be considered a random sample of the future. This data are thenplotted on a probability plot. If the data follow a straight line, thedata are presumed to be from the distribution associated with theprobability plot; i.e., normal in this case. From this individualcontrol chart, one notes that no improvements occurred as the stoplightscore-carding had indicated. The probably plot indicates an expectationthat about 27% of the months we should expect not to achieve our goal of1.41 or less.

Let's now consider this goal setting in objective. When goal setting forthis particular type of measurement would it be better to set haveSpecific Measurable Actionable Relevant, and Time-based goal setting(i.e., SMART goal setting) based on the mean? If this were done, onewould tend to give focus to the process and not what happenedspecifically during the latest time period. With 30,000-foot-levelperformance metric 304, rather than report-out proportion non-compliantper month, one could report-out the expected mean with an 80% frequencyof occurrence. For this particular situation, this methodology couldprovide a good baseline for setting goals. See FIG. 19 which FIG. 19illustrates another individuals chart with a probability chart for thecurrent scenario, along with a predicted process median, and 80%frequency of occurrence rate when no process specification exists.Again, a prediction statement is included at the bottom of the chart.

This form of reporting for this particular situation would be verydesirable. From this form of reporting, one would begin to view thatthis metric 304 as the result of variability in the process 301 and thatimprovement activities are needed to reduce the scrap costs from theprocess 301. A Pareto chart of the types of failures that occurred inthe recent region of stability could provide insight to what issuesoccur most frequently. This insight can be very beneficial to targetareas of the process 301 that might cause these defectives occurrences.

Quality Cost Production ($/unit): Goal Setting Scenario 2

The line item from the goal setting worksheet scenarios for thismeasurement is shown in FIG. 20. FIG. 20 illustrates yet anothertraditional stoplight scorecard for one scenario. There is a lot of redin this goal setting scenario. Let's now evaluate this metric 304 from a30,000-foot-level point of view as illustrated by FIG. 21. FIG. 21illustrates an individuals chart (I-chart) and a probability chart as analternative report-out to FIG. 20, where this charting indicates thatthe process is not predictable, since a data point is beyond an uppercontrol limit (UCL) for this scenario. A prediction statement decisionsis included at the bottom of the chart.

This report-out indicates that there was a special-cause situation inthe chart that warrants investigation. However, one should not react toall the ups and downs of the metric in the statistically calculatedupper and lower control limit regions (UCL and LCL), which are functionof the collected data and its variability; i.e., not what desired. Notehow this report-out provides a very different perspective about theprocess than the stoplight scorecard goal setting theory application.

One could stage the process 301 at the special cause region to examinehow the process 301 is performing since that the special causecondition. One perhaps did this assessment after understanding thespecial cause occurrence and then made adjustments so this particularoccurrence does not happen again. The result from this effort would beis shown in FIG. 22. FIG. 22 illustrates another I-chart and anotherprobability chart pair for a scenario where a stage was created when itwas believed that the process output shown in FIG. 21 had changed andthere was a specification requirement. A prediction statement isincluded at the bottom of the chart.

Using the value in the goal setting worksheets for the monthly goal, onecould state that the process is now stable where about 55% of the monthswill not meet the monthly objective. Again, since this goal is notnecessarily a specification, a median best-estimate report-out with 80%frequency of occurrence seems to be a better report-out method.

FIG. 23 seems to be a good baseline to act as a goal setting tool fromwhich SMART goal setting can occur. FIG. 23 illustrates yet anotherI-chart and yet another probability chart for a scenario where a stagewas created when it was believed that the process output shown in FIG.21 had changed and there was NOT a specification requirement. (estimatedmedian and 80% frequency of occurrence reported) A prediction statementis included at the bottom of the chart. Again, with this approach wouldbe given to improve the process as opposed to reacting to all the upsand downs in the common-cause stability region.

Defects (ppm): Goal Setting Scenario 3

For defects this business goal setting worksheet scenarios, a goal of1000 parts per million (PPM) was given. As FIG. 24 shows, there is muchred in this goal setting item. FIG. 24 illustrates another traditionalstoplight scorecard for one scenario. Since the data from this scenarioare pass/fail attribute data, a probability plot should not be createdfor the 30,000-foot-level report-out. A 30,000-foot-level attributeassessment for this data would be as shown in FIG. 25. The gap in theplot occurred because the raw data set had a missing datum point.

The estimated performance for this chart was determined from thecenterline of FIG. 25. FIG. 25 illustrates an I-chart for the the FIG.24 scenario, which involves attribute data, where a prediction statementis included at the bottom of the chart. One can see that the process 301is producing about 4327 ppm units, where there was a goal of 1000 ppm.However, note how the value of zero is within the UCL and LCL limits.When this occurs, one should examine what is getting measured. If themeasurement is bounded by zero, this indicates that a datatransformation may be necessary to establish stability and quantifypredictability; however, a transformation should make physical sense.

For this data, an assessment indicated that a log transformation wasappropriate. The result of this effort is shown in FIG. 26. FIG. 26illustrates an I-chart for the FIG. 24 scenario, which involvesattribute data which was transformed, where a prediction statement isincluded at the bottom of the chart.

When one has a transformation, they have no need to address thetransformed units on the y-axis since when the process is stable aperformance metric 304 statement will be made at the bottom of thechart. The reader should note that even though there was additionalcomplexity when creating the chart, the interpretation of the graphicaloutput is similar to previous outputs and easy to understand.

The question one could ask is whether the above goal setting in processapplication is for the overall process 301 n mean or individual months.For this type situation, a statement of how well the process 303 isperforming would be best, understanding that some months could have ahigher reporting and others a lower one.

Total Unclean Sales Orders %: Goal Setting Scenario 4

The goal for this measurement is 8. For this measurement from the goalsetting spreadsheet scenarios, there are many red points as shown byFIG. 27. FIG. 27 illustrates another traditional stoplight scorecard fora scenario. An attribute 30,000-foot-level predictive performance metric304 for this measurement from the business goal setting worksheetscenarios would be as shown in FIG. 28. FIG. 28 illustrates an I-chartfor the FIG. 27 scenario with the inclusion of a prediction statement.The same discussion points that were made in the last data-plotsituation can again be made for this plot. This plot could also betransformed because of the zero boundary; however, this was not donesince the zero value is within the UCL and LCL limits. This process 302indicates stability and that if the predicted response is undesirable asystematic process improvement effort is needed.

Avoidable Unclean Sales Orders-%: Goal Setting Scenario 5

Five was the result of the organization's goal setting effort for thismeasurement. There are many red occurrences in this goal settingtemplate response as shown by FIG. 29. FIG. 29 illustrates a traditionalstoplight scorecard for one scenario. A 30,000-foot-level attributereport-out for this data set is shown in FIG. 30. FIG. 30 illustrates anI-chart for an attribute (pass/fail situation), where the process isbelieve stable/predictable and the prediction statement is included atthe bottom of the chart.

One could have undertaken using a transformation for this report-out;however, this was not done since the zero value is barely inside theLCL. Similar to previous report-outs this process response is stable andhas common-cause variation that, if considered unacceptable, wouldresult in these metric 304 enhancement needs pulling for a processimprovement effort.

First Pass Yield (FPY) %: Goal Setting Scenario 6

The goal for first pass yield (FPY) is 96%. From this business goalsetting worksheet scenarios illustration, we note the data shown in FIG.31. FIG. 31 illustrates another traditional stoplight scorecard. Thisgoal setting theory for reporting methodology indicates that everythingappears to be in good order. Let's now examine the data in a30,000-foot-level report-out format as shown in FIG. 32. FIG. 32illustrates an I-chart alternative to FIG. 31 which provides moreinformation about the process and its variability. Since the process isstable one can conclude that the process is predictable, where theprediction statement is noted at the bottom of the chart. From this goalsetting in report-out, the process 302 appears stable with an estimatedfirst pass yield of 96.7%. One should note that for a percentageresponse and if 100% is within the control limits, one should considerchanging the response from percent acceptable to percent non-acceptable,where an appropriate transformation would be selected.

For this situation, the goal of 96% FPY was achieved; however, one mightask whether this was a SMART goal that benefited for the organization asa whole. If a higher yield would seem to be beneficial, improvementefforts would then be appropriate.

In summary, of the foregoing, non-limiting examples, for these businessgoal setting scenarios, the following observations can be considered. Inall the above comparison plots, a different decision was made about theprocess relative to actions or non-actions for the output formats. Infive out of the six shown goal setting worksheet issues, there was aswitching between green and red where the 30,000-foot-level reportingindicated that these transitions were from common-cause variability.Stoplight score-carding can lead to unhealthy if not destructivebehaviors that can cost an organization a lot of money. Organizationsgain much from viewing the output of their process at the30,000-foot-level.

There is a tendency for reporting annually, as the charts above did;however, one should not be bounded by the calendar when creating 30,000foot-level reporting. Several years of data could provide much moreinsight than a short calendar-year plot. Organizations can look at theirmetrics 304 collectively to determine what metrics 304 need focus soprocess improvement efforts benefit the enterprise as a whole would begiven to these areas of the business. Organizations do not have enoughresources to do a significant amount of improvement for everything. Theabove 30,000-foot-level approach for metric 304 tracking throughout theorganization can lead to improved SMART goal setting.

An organization can have their metrics 304 automatically reported at the30,000-foot-level using EPRS software. Updates could be made daily.Those who have authorization and Internet/network access could get up todate information about their predictive performance metrics, where thereis an alignment to an organization's IEE value chain 300. The dynamicsof status meetings can change to the better when an organization's valuechain 300 and its associated 30,000-foot-level metrics 304 arereferenced during the meeting instead of giving a sole focus to theissues of the day.

Note also, that while some illustrative metrics 304 are disclosed above,these scenarios are non-limiting. But they do show how metrics 304 canbe programmed into systems of embodiments.

As alluded to above, in some situations upper management (and/or otherpersonnel) might not want to know the “truth” about a given situation.The reasons vary but bad news could impact their bonuses for meeting thenext quarter's goals. Plus, or in the alternative, they might not wantto be the bearer of because doing so could be career limiting. EPRS®systems disclosed herein provide transparency with respect to metricsthat personnel involved agreed to before situations develop and whichare automatically reported from a process point of view. Metricsreported in accordance with embodiments cannot be gamed. Nor canrecipients of bad news “kill the messenger” because these metrics arereported by systems of embodiments without user involvement. Users atall levels and who have read-privileges can view the system-reportedmetrics for various processes at any time, not just at the end of thequarter or month for executive periodic “reviews”. The “truth” relativeto how processes are being executed and their performance cannot be“played with” to make situations look better than they are (from aprocess point of view).

As disclosed herein, embodiments provide systems report performancemetrics which are integrated with the processes from which they aroseand which are reported from a process point of view. Systems ofembodiments also enable users to implement process improvement effortsthat benefit their respective organizations at the “big picture” level.Moreover, the metric improvement need pulls for an improvement projectcreation

CONCLUSION

Although the subject matter has been disclosed in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts disclosed above.Rather, the specific features and acts described herein are disclosed asillustrative implementations of the claims.

1. A system for monitoring a physical process, the system comprising: aprocessor co-located with the physical process and being configured toaccept a safety-related metric regarding the physical process; a networkin communication with the co-located processor; a processor locatedremotely from the physical process and in communication with thenetwork, the remote processor being configured to receive thesafety-related metric regarding the physical process from the co-locatedprocessor, to transform the safety-related metric regarding the physicalprocess to a probability metric, and to determine, based on theprobability metric, whether the physical process is predictable; and anoutput device in communication with the remote processor wherein theremote processor is further configured to output the safety-relatedpredictability determination regarding the physical process via theoutput device.
 2. The system of claim 1 wherein the physical process isassociated with a petrochemical well.
 3. The system of claim 1 whereinthe physical process is one of a food or beverage production process. 4.The system of claim 1 further comprising a memory in communication withthe remote processor, the memory being configured to store a flowchartrepresentation of the physical process and wherein the co-locatedprocessor is further configured to associate the probability metric withthe physical process.
 5. The system of claim 1 further comprising amemory in communication with the remote processor, the memory beingconfigured to store a flowchart representation of the physical processand wherein the co-located processor is further configured to associatethe safety-related metric with the physical process.
 6. A system formonitoring a physical process, the system comprising: a processorco-located with the physical process and being configured to accept asafety-related metric regarding the physical process; a network incommunication with the co-located processor; a processor locatedremotely from the physical process and in communication with thenetwork, the remote processor being configured to receive thesafety-related metric from the co-located processor, to transform thesafety-related metric to a probability metric, and to determine, basedon the probability metric, whether the physical process is predictable;and an output device in communication with the remote processor whereinthe remote processor is further configured to output the safety-relatedpredictability determination regarding the physical process via theoutput device.
 7. The system of claim 6 wherein the physical process isassociated with a petrochemical well.
 8. The system of claim 6 whereinthe physical process is one of a food or beverage production process. 9.The system of claim 6 further comprising a memory in communication withthe remote processor, the memory being configured to store a flowchartrepresentation of the physical process and wherein the co-locatedprocessor is further configured to associate the probability metric withthe physical process.
 10. The system of claim 6 further comprising amemory in communication with the remote processor, the memory beingconfigured to store a flowchart representation of the physical processand wherein the co-located processor is further configured to associatethe safety-related metric with the physical process.
 11. A system formonitoring a process, the system comprising: a processor co-located withthe process and being configured to accept a metric regarding theprocess; a network in communication with the co-located processor; aprocessor located remotely from the process and in communication withthe network, the remote processor being configured to receive the metricfrom the co-located processor, to transform the metric to a probabilitymetric, and to determine, based on the probability metric, whether theprocess is predictable; and an output device in communication with theremote processor wherein the remote processor is further configured tooutput the predictability determination regarding the process via theoutput device.
 12. The system of claim 11 wherein the process isassociated with a petrochemical well.
 13. The system of claim 11 whereinthe process is one of a food or beverage production process.
 14. Thesystem of claim 11 further comprising a memory in communication with theremote processor, the memory being configured to store a flowchartrepresentation of the process and wherein the co-located processor isfurther configured to associate the probability metric with an operationof the process.
 15. The system of claim 11 further comprising a memoryin communication with the remote processor, the memory being configuredto store a flowchart representation of the process and wherein theco-located processor is further configured to associate the metric withthe process.
 16. The system of claim 11 wherein the metric is asafety-related metric.